This disclosure relates generally to the field of image processing and, more specifically, to a method and apparatus for three-dimensional image reconstruction and processing.
One way that three dimensional computer models of a scene can be created is from photographs. This method uses stereo triangulation to measure the distance to various points in the images. The baseline for this triangulation can be produced by two widely separated cameras, or by one camera with relative motion between the camera and the subject. A second method uses perspective geometry to reconstruct the scene in three dimensions if there are strong perspective lines in the image. For example, renaissance paintings, with well defined perspective lines, have been used to create three dimensional computer models of the scene in the painting. This is done by mapping the perspective lines which converge to their vanishing points and interpolating the material in between the perspective lines.
Converting a painting, or a photograph, into a three dimensional model of the represented scene is usually done by using the information inherent in perspective lines to define the geometry of the scene. For example, Renaissance painters often showed receding intersections of walls, panels, floor tiles and roof or ceiling structures. These receding elements converged to well defined vanishing points according to the then newly discovered rules of perspective geometry. In recent times these rules of perspective geometry have been worked backward to develop three dimensional models of some of these Renaissance paintings.
Up until now, extracting the perspective information from an image that is needed to reconstruct three dimensional models from that image has disadvantages. There are two techniques commonly in use. The first is to trace, by hand, the major perspective lines in an image. These tracings are used to define the vanishing points from which a three dimensional model can be constructed. The second method partially automates this process by developing vectors which correspond to the major perspective lines in an image. These vectors are then extended to meet at the vanishing points which define the scene geometry. Unfortunately, existing techniques for measuring these perspective lines are not very accurate and the derived perspective vectors usually do not meet at small points. Rather, the derived lines typically only partially converge and special mathematical methods, such as Singular Value Decomposition (SVD) are needed to “force” convergence to a point.
What is needed, among other things, is an improved method for determining the orientation of a camera (i.e. camera pose) from various characteristics of moving and stationary images and to be able to construct models of three-dimensional images from other images.